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  <div class="section" id="actor-critic">
<h1>Actor-Critic<a class="headerlink" href="#actor-critic" title="Permalink to this headline">¶</a></h1>
<p><strong>Actions space:</strong> Discrete | Continuous</p>
<p><strong>References:</strong> <a class="reference external" href="https://arxiv.org/abs/1602.01783">Asynchronous Methods for Deep Reinforcement Learning</a></p>
<div class="section" id="network-structure">
<h2>Network Structure<a class="headerlink" href="#network-structure" title="Permalink to this headline">¶</a></h2>
<a class="reference internal image-reference" href="../../../_images/ac.png"><img alt="../../../_images/ac.png" class="align-center" src="../../../_images/ac.png" style="width: 500px;" /></a>
</div>
<div class="section" id="algorithm-description">
<h2>Algorithm Description<a class="headerlink" href="#algorithm-description" title="Permalink to this headline">¶</a></h2>
<div class="section" id="choosing-an-action-discrete-actions">
<h3>Choosing an action - Discrete actions<a class="headerlink" href="#choosing-an-action-discrete-actions" title="Permalink to this headline">¶</a></h3>
<p>The policy network is used in order to predict action probabilites. While training, a sample is taken from a categorical
distribution assigned with these probabilities. When testing, the action with the highest probability is used.</p>
</div>
<div class="section" id="training-the-network">
<h3>Training the network<a class="headerlink" href="#training-the-network" title="Permalink to this headline">¶</a></h3>
<p>A batch of <span class="math notranslate nohighlight">\(T_{max}\)</span> transitions is used, and the advantages are calculated upon it.</p>
<p>Advantages can be calculated by either of the following methods (configured by the selected preset) -</p>
<ol class="arabic simple">
<li><p><strong>A_VALUE</strong> - Estimating advantage directly:
<span class="math notranslate nohighlight">\(A(s_t, a_t) = \underbrace{\sum_{i=t}^{i=t + k - 1} \gamma^{i-t}r_i +\gamma^{k} V(s_{t+k})}_{Q(s_t, a_t)} - V(s_t)\)</span>
where <span class="math notranslate nohighlight">\(k\)</span> is <span class="math notranslate nohighlight">\(T_{max} - State\_Index\)</span> for each state in the batch.</p></li>
<li><p><strong>GAE</strong> - By following the <a class="reference external" href="https://arxiv.org/abs/1506.02438">Generalized Advantage Estimation</a> paper.</p></li>
</ol>
<p>The advantages are then used in order to accumulate gradients according to
<span class="math notranslate nohighlight">\(L = -\mathop{\mathbb{E}} [log (\pi) \cdot A]\)</span></p>
<dl class="class">
<dt id="rl_coach.agents.actor_critic_agent.ActorCriticAlgorithmParameters">
<em class="property">class </em><code class="sig-prename descclassname">rl_coach.agents.actor_critic_agent.</code><code class="sig-name descname">ActorCriticAlgorithmParameters</code><a class="reference internal" href="../../../_modules/rl_coach/agents/actor_critic_agent.html#ActorCriticAlgorithmParameters"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#rl_coach.agents.actor_critic_agent.ActorCriticAlgorithmParameters" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>policy_gradient_rescaler</strong> – (PolicyGradientRescaler)
The value that will be used to rescale the policy gradient</p></li>
<li><p><strong>apply_gradients_every_x_episodes</strong> – (int)
The number of episodes to wait before applying the accumulated gradients to the network.
The training iterations only accumulate gradients without actually applying them.</p></li>
<li><p><strong>beta_entropy</strong> – (float)
The weight that will be given to the entropy regularization which is used in order to improve exploration.</p></li>
<li><p><strong>num_steps_between_gradient_updates</strong> – (int)
Every num_steps_between_gradient_updates transitions will be considered as a single batch and use for
accumulating gradients. This is also the number of steps used for bootstrapping according to the n-step formulation.</p></li>
<li><p><strong>gae_lambda</strong> – (float)
If the policy gradient rescaler was defined as PolicyGradientRescaler.GAE, the generalized advantage estimation
scheme will be used, in which case the lambda value controls the decay for the different n-step lengths.</p></li>
<li><p><strong>estimate_state_value_using_gae</strong> – (bool)
If set to True, the state value targets for the V head will be estimated using the GAE scheme.</p></li>
</ul>
</dd>
</dl>
</dd></dl>

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